Abiel,
Thanks a lot for the report.
There could be some improvement to do on ma.apply_along_axis, but I'm
afraid I won;'t be able to work on it any time soon. I'll try to find
a substitute to apply_along_axis in the mean time, we'll keep you
posted.
Personally, I rarely use the func parameter of convert, preferring to
deal directly w/ the 2D series a basic .convert(.., func=None)
outputs. It's quite useful when you have several operations to perform
on the same series (eg, getting the mean & std deviation...), as you
convert the series only once.
Note that in your example, you shouldn't have to recreate a series:
qt = t.convert(freq="Q")
mqt = qt.mean(-1)
Mmh. We really need to get these "Examples/FAQ" sections in the doc...
Cheers and thx again
P.
On Apr 7, 2009, at 12:17 PM, Abiel X Reinhart wrote:
> I have recently begun working with the useful scikits.timeseries
> package, and noticed some performance issues in the ts.convert()
> function. For example, when converting 1000 monthly values to a
> quarterly frequency using the ma.mean() function, it took me about
> 0.6 seconds. This isn't that bad, but it definitely can become an
> issue when working with many series or longer timespans.
>> After looking at the scikits.timeseries source code, I found
> essentially all the delay was coming from the ma.apply_along_axis()
> call inside _convert1d() function. I am not that familiar with the
> numpy functions, but it seems that ma.apply_along_axis can be be
> rather slow. For instance, consider the following code:
>> a = np.arange(300000).reshape(30000,10)
> b = ma.mean(a,-1)
> c = ma.apply_along_axis(ma.mean, -1, a)
>> In this example, b = c, but b is generated much quicker. My system
> was always able to generate b in less than 0.02 seconds. but took
> about 4.3 seconds to generate c.
>> Perhaps an improvement could be made to the convert() function by
> recognizing a standard set of built-in numpy functions like ma.mean
> and applying the method used to generate "b" above, and only using
> ma.apply_along_axis() for custom functions. Since I imagine most
> people use standard aggregation functions like ma.mean and ma.sum,
> this could lead to a big speed improvement. I am building a GUI
> application, and this would make the difference between an
> application that reacts essentially instantly and one that hangs
> slightly in many situations.
>> One other possible solution seems to be leave scikits.timeseries
> unchanged, and do something like the following:
>> Let t be a monthly time series.
>> t = t.convert(freq="Q")
> t = ts.time_series(ma.mean(t,-1), freq="Q", start_date=t.start_date)
>> The downside of this is its just more verbose, and many users may
> not even think of it.
>> Thanks very much.
>> Abiel
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